Topic: topological connectivity

Cognitive maps have been proposed as an essential relational structure for human memory and thought (Tolman, 1948; Constantinescu et al., 2016). In this format, remembered entities are embedded in space and their relationships are encoded via concrete angles and Euclidean distances. Although much of higher human cognition may be spatial, cognitive maps are not the only possible relational structure. With a behavioral experiment we aim to challenge the extent of the cognitive map hypothesis and to investigate when and how humans represent knowledge via the relational structure of cognitive grap

Automated axon tracing via fully supervised learning requires annotated 3D brain imagery, which necessitates expertise and labor to produce. Reducing the amount of annotated training data needed for supervised algorithms for axon tracing would expedite critical brain research. In this work, we developed and integrated a self-supervised learning (SSL) algorithm trained on large unannotated data volumes into NeuroTrALE (pictured), a custom AI-driven pipeline for automatic annotation and visualization of 3D brain imagery. Our light sheet microscopy dataset consisted of stained Parvalbumin positiv